shanghai-academy-of-ai-for-science/mkb — explained in plain English
Analysis updated 2026-05-18
Analyze protein, RNA, or DNA sequences with one shared scientific AI model
Design new RNA sequences or generate molecule structures from text descriptions
Forecast global weather ten days ahead using the model's Earth science mode
Segment medical scan images like CT, MRI, or X-ray using text prompts
| shanghai-academy-of-ai-for-science/mkb | abhisumatk/epstein_files_rag | asdfo123/forgewm | |
|---|---|---|---|
| Stars | 34 | 34 | 34 |
| Language | Python | Python | Python |
| Setup difficulty | hard | moderate | hard |
| Complexity | 5/5 | 3/5 | 5/5 |
| Audience | researcher | researcher | researcher |
Figures from each repo's GitHub metadata at analysis time.
Needs an NVIDIA GPU with at least 48GB of memory, a pinned transformers version, and separately downloaded model weights.
MKB, also called Monkey King Bang, is a single AI model built to understand and generate scientific data across many different fields at once, including Earth weather patterns, proteins, RNA, DNA, and small molecules. Instead of needing a separate specialized model for each of these areas, it combines them all into one shared foundation built around an existing large language model called Qwen3-VL, with about 11 billion parameters in total once its extra scientific components are added in. The key design idea is that each type of scientific data keeps its own specialized reader and writer, tuned to that data's particular shape, while all of them share the same central AI backbone. For example, protein sequences use a dedicated protein reader called ESM-2, molecules use a graph based reader, weather data uses a component built for gridded map style data, and medical images use a component based on Meta's SAM image segmentation system. Each of these can also produce output in its native form, whether that is a classification, a designed sequence, a chemical structure written as text, a weather forecast, or a medical image segmentation mask, all accessed through plain natural language instructions rather than separate specialized interfaces for each science. The README backs this up with detailed benchmark comparisons against other scientific AI models, and reports that MKB matches or beats a model roughly 100 times larger on about half of twenty biological understanding tests, while also outperforming or closely matching a leading physics based weather forecasting system used operationally by European forecasters, and matching or beating specialized medical image segmentation tools across most of nine types of medical imaging. Running it yourself is demanding: it needs Python 3.10, an NVIDIA graphics card with at least 48 gigabytes of memory, and a specific CUDA and library setup, with an optional faster attention mode you can compile separately if you want extra speed. Model weights are downloaded separately after installing the required Python packages. It is written in Python and released under a combination of the Apache 2.0 license and Meta's SAM license, reflecting that it reuses parts of Meta's segmentation model.
A single AI foundation model that understands and generates scientific data across proteins, RNA, DNA, molecules, weather, and medical images through one shared backbone.
Mainly Python. The stack also includes Python, PyTorch, Qwen3-VL.
Mostly permissive Apache 2.0 terms, but the medical image segmentation part follows Meta's separate SAM license with its own restrictions.
Setup difficulty is rated hard, with roughly 1day+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Don't trust strangers blindly. Verify against the repo.